Graphics in R
Setup
Run the Setup.R file.
If everything works correctly, you should see a plot: 
Basic Scatterplot
Basic scatter plot of diamond price vs. carat weight
qplot(carat, price, geom = "point", data = diamonds)
Another Scatterplot
Scatter plot of diamond price vs carat weight showing versitility of options in qplot
qplot(carat, log(price), geom = "point", data = diamonds,
alpha = I(0.2), color = color,
main = "Log price by carat weight, grouped by color") +
xlab("Carat Weight") + ylab("Log Price")
Solutions
- Scatterplot of variables tips and total bill
qplot(data = tips, x = total_bill, y = tip)
Solutions
- Color points by smokers
qplot(data = tips, x = total_bill, y = tip, color = smoker)
Solutions
- Pretty axis lables and title
qplot(data = tips, x = total_bill, y = tip, color = smoker,
xlab = "Total Bill ($)", ylab = "Tip ($)",
main = "Tip left by patrons' total bill and smoking status")
A Basic Map
A bunch of latitude longitude points…
qplot(long, lat, geom = "point", data = states)
A Bit Better Map
… that are connected with lines in a very specific order.
qplot(long, lat, geom = "path", data = states, group = group) +
coord_map()
Polygon vs Path
qplot(long, lat, geom = "polygon", data = states, group = group) +
coord_map()
Polygon vs Path
qplot(long, lat, geom = "polygon",
fill = I("white"), color = I("black"),
data = states, group = group) +
coord_map()
Plotting the Result
qplot(long, lat, geom = "polygon", data = states.class.map,
group = group, fill = StateGroups, color = I("black")) +
coord_map() 
Shade and Intensity
Average # of days in the last 30 days of insufficient sleep
qplot(long, lat, geom = "polygon", data = states.map,
group = group, fill = avg.qlrest2) + coord_map()
Adding Information
Average # of alcoholic drinks per day by state and gender
qplot(long, lat, geom = "polygon", data = states.sex.map,
group = group, fill = avg.drnk) + coord_map() +
facet_grid(sex ~ .)
Solutions
Cleaned Up Map
Solutions
Deepwater Horizon Oil Spill
Floats
qplot(Longitude, Latitude, color = callSign, data = floats) +
coord_map()
Gliders
qplot(Longitude, Latitude, color = callSign, data = gliders) +
coord_map()
Boats
qplot(Longitude, Latitude, color = callSign, data = boats) +
coord_map()
Layers Preview
ggplot() +
geom_path(data = states, aes(x = long, y = lat, group = group)) +
geom_point(data = floats, aes(x = Longitude, y = Latitude, color = callSign)) +
geom_point(aes(x, y), shape = "x", size = 5, data = rig) +
geom_text(aes(x, y), label = "BP Oil Rig",
size = 5, data = rig, hjust = -0.1) +
xlim(c(-91, -80)) + ylim(c(22,32)) + coord_map()
Brevity
Even ggplot will automatically pick default scales:
ggplot(data = floats,
aes(x = Longitude, y = Latitude, color = callSign)) +
geom_point()
Your Turn
Find the ggplot() statement that creates this plot:
Hint: look at the Floats data for variable ideas
Solutions
ggplot(aes(x = Depth, y = Temperature, color = callSign),
data = floats) +
geom_point()
The Result
ggplot() +
geom_path(data = states, aes(x = long, y = lat, group = group)) +
geom_point(data = floats, aes(x = Longitude, y = Latitude, color = callSign)) +
geom_point(aes(x, y), shape = "x", size = 5, data = rig) +
geom_text(aes(x, y), label = "BP Oil Rig", size = 5, data = rig, hjust = -0.1) +
xlim(c(-91, -80)) +
ylim(c(22, 32)) + coord_map()
Solutions
- Plot the location of animal sightings on a map
Solutions
- On this plot, try to color points by class of animal and/or status of animal
Solutions
- On this plot, try to color points by class of animal and/or status of animal
Solutions
- Advanced: Is there a way to indicate time?
Cost of an Education
Pre-Attentive Features
- Things that “jump out” in less than 250 ms
- Color, form, movement, spatial localization
Pre-Attentive Features
Hierarchy of Features
- Color is stronger than shape
- Combinations of pre-attentive features are usually not pre-attentive due to interference
Hierarchy of Features
- Color is stronger than shape
- Combinations of pre-attentive features are usually not pre-attentive due to interference
Your Turn
Find ways to improve the following graphic:
frame <- read.csv("https://bit.ly/2i3Q4Gf")
qplot(x, y, data = frame, shape = g1, colour = g2, size = I(4))
- Make sure the “oddball” stands out while keeping the information on the groups
- Hint:
interactioncombines factor variables
Solutions
# Make sure the "oddball" stands out while keeping the
# information on the groups
frame$inter <- interaction(frame$g1, frame$g2)
ggplot(frame, aes(x, y)) +
geom_point(aes(shape = g1, color = inter), size = I(4))
Solutions
# Make sure the "oddball" stands out while keeping the
# information on the groups
frame$inter <- interaction(frame$g1, frame$g2)
ggplot(frame, aes(x, y)) +
geom_point(aes(shape = g1, fill = g2, color = inter), size = I(4), stroke = I(2)) +
scale_shape_manual(values = c(21,23)) +
scale_fill_manual(values = c("red", "green")) +
scale_colour_manual(values = c("red", "black", "green")) +
guides(fill = guide_legend(override.aes = list(color = c("red", "green"))),
colour = guide_legend(override.aes = list(fill = "white", shape = 22)))
Aesthetics in ggplot2
Main parameters: alpha, shape, color, size
Ordering Variables
- Position:
higher is larger (y), items to the right are larger (x) - Size, Area
- Color: not always ordered. More contrast = larger
- Shape: Unordered.
More Color
Color is context-sensitive:
More Color
Color is context-sensitive:
A and B are the same intensity and hue, but appear to be different.
More Color
Color is context-sensitive:
A and B are the same intensity and hue, but appear to be different.
Gradients
Qualitative schemes: no more than 7 colors
Quantitative schemes: use color gradient with only one hue for positive values
More Gradients
Quantitative schemes: use color gradient with two hues for positive and negative values.
Gradient should go through a light, neutral color (white) corresponding to 0.
Small objects or thin lines need more contrast than larger areas
RColorBrewer
R package based on Cynthia Brewer’s color schemes (http://www.colorbrewer2.org)
Your Turn
- In the diamonds data, cut is ordinal, while price and carat are continuous
- Find a graphic that gives an overview of these three variables while respecting their types
- Hint: Start with the following code
qplot(carat, price, colour = clarity, data = diamonds)
Solutions
qplot(carat, price, colour = clarity, data = diamonds) +
scale_colour_brewer(palette = "BuGn")
Facetting
qplot(price, carat, data = diamonds, color = color,
facets = . ~ clarity)
Solutions
ggplot(movies, aes(x = year, y = budget,
group = genre, color = genre)) +
geom_point()
Solutions
ggplot(movies, aes(x = year, y = budget,
group = genre, color = genre)) +
geom_point(alpha = I(.2)) +
facet_wrap(~genre)
Solutions
ggplot(movies, aes(x = genre, fill = mpaa)) + geom_bar() 
Solutions
ggplot(movies, aes(x = year, y = length,
group = genre, color = genre)) +
geom_smooth(fullrange = F) +
coord_cartesian(ylim = c(0, 150))
Solutions
ggplot(movies, aes(x = budget, y = rating, group = genre)) +
geom_point(alpha = .1) +
facet_grid(mpaa ~ genre) +
geom_smooth(method = "lm", se = F) +
scale_x_log10()
Plot Title
qplot(carat, price, data = diamonds) +
ggtitle("Price vs Carat for Diamonds")
Built-In Themes
qplot(carat, price, data = diamonds)
qplot(carat, price, data = diamonds) + theme_bw()
Modifying a plot
p <- qplot(carat, price, data = diamonds) +
ggtitle("Price vs Carat for Diamonds")
p + theme(plot.title = element_text(colour = "red", angle = 20))
Use this power wisely
Removing Axes
It’s also possible to remove all axes (helpful for maps):
p + theme(
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.length = unit(0, "cm")
)
Saving your Work
The ggsave() function will save the last plot produced:
qplot(total_bill, tip, data = tips)
ggsave("tips.png")
ggsave("tips.pdf")
ggsave("tips.png", width = 6, height = 6)
